{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.05636211  0.52055999 -0.51833213  0.05925254]\n",
      " [ 0.09934507  0.84938075 -0.31684873 -0.33688814]\n",
      " [-1.0795973   0.74194403  0.5596549   0.73646054]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.05636211,  0.52055999],\n",
       "       [ 0.09934507,  0.84938075],\n",
       "       [-1.0795973 ,  0.74194403]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.random.randn(3,4)\n",
    "\n",
    "print(a)\n",
    "\n",
    "a[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = np.random.randn(1024,640,3)\n",
    "img[512:,:320,:]\n",
    "\n",
    "img[1::2,::2]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py310",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
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